{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titannic 生存分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据总览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(r'D:\\code\\project\\dp_models\\datasets\\titannic\\train.csv')\n",
    "test_data = pd.read_csv(r'D:\\code\\project\\dp_models\\datasets\\titannic\\test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
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       "2            894       2                     Myles, Mr. Thomas Francis   \n",
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     "execution_count": 4,
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.set_style('whitegrid') # ？\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "-!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!--!-\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "train_data.info()\n",
    "print(\"-!-\" * 40)\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面我们可以看出，Age、Cabin、Embarked、Fare几个特征存在缺失值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* labeldistance，文本的位置离远点有多远，1.1指1.1倍半径的位置\n",
    "* autopct，圆里面的文本格式，%3.1f%%表示小数有三位，整数有一位的浮点数\n",
    "* shadow，饼是否有阴影\n",
    "* startangle，起始角度，0，表示从0开始逆时针转，为第一块。一般选择从90度开始比较好看\n",
    "* pctdistance，百分比的text离圆心的距离\n",
    "* patches, l_texts, p_texts，为了得到饼图的返回值，p_texts饼图内部文本的，l_texts饼图外label的文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Survived'].value_counts().plot.bar(xlabel = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Survived'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Survived'].value_counts().plot.pie(labeldistance = 1.1, autopct = '%1.2f%%', shadow = False, startangle = 90, pctdistance = 0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据进行分析的时候要注意其中是否有缺失值。一些机器学习算法能够处理缺失值，比如神经网络，一些则不能。\n",
    "对于缺失值，一般有以下几种处理方法:\n",
    "\n",
    "1. 如果数据集很多，但有很少的缺失值，可以删掉带缺失值的行；\n",
    "2. 如果该属性相对学习来说不是很重要，可以对缺失值赋均值或者众数。\n",
    "3. 对于标称属性，可以赋一个代表缺失的值，比如‘U0’。因为缺失本身也可能代表着一些隐含信息。比如船舱号Cabin这一属性，缺失可能代表并没有船舱。\n",
    "\n",
    "比如在哪儿上船Embarked这一属性（共有三个上船地点），缺失俩值，可以用众数赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data.Embarked[train_data.Embarked.isnull()]\n",
    "\n",
    "# train_data.Embarked.dropna().mode().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.Embarked[train_data.Embarked.isnull()] = train_data.Embarked.dropna().mode().values\n",
    "#replace missing value with U0\n",
    "train_data['Cabin'] = train_data.Cabin.fillna('U0')    \n",
    "#train_data.Cabin[train_data.CAbin.isnull()]='U0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用回归 随机森林等模型来预测缺失属性的值。因为Age在该数据集里是一个相当重要的特征（先对Age进行分析即可得知），所以保证一定的缺失值填充准确率是非常重要的，对结果也会产生较大影响。一般情况下，会使用数据完整的条目作为模型的训练集，以此来预测缺失值。对于当前的这个数据，可以使用随机森林来预测也可以使用线性回归预测。这里使用随机森林预测模型，选取数据集中的数值属性作为特征（因为sklearn的模型只能处理数值属性，所以这里先仅选取数值特征，但在实际的应用中需要将非数值特征转换为数值特征）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#choose training data to predict age\n",
    "age_df = train_data[['Age','Survived','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "age_df_notnull = age_df.loc[(train_data['Age'].notnull())]\n",
    "age_df_isnull = age_df.loc[(train_data['Age'].isnull())]\n",
    "X = age_df_notnull.values[:,1:]\n",
    "Y = age_df_notnull.values[:,0]\n",
    "\n",
    "# use RandomForestRegression to train data\n",
    "RFR = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
    "RFR.fit(X,Y)\n",
    "predictAges = RFR.predict(age_df_isnull.values[:,1:])\n",
    "train_data.loc[train_data['Age'].isnull(), ['Age']]= predictAges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们再来看一下缺失数据处理后的DataFram：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        891 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 分析数据关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 性别与是否生存的关系 Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[['Sex','Survived']].groupby(['Sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex', 'Survived']].groupby(['Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上为不同性别的生存率，可见在泰坦尼克号事故中，还是体现了Lady First.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 船舱等级和生存与否的关系 Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass  Survived\n",
      "1       0            80\n",
      "        1           136\n",
      "2       0            97\n",
      "        1            87\n",
      "3       0           372\n",
      "        1           119\n",
      "Name: Pclass, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data.groupby(['Pclass','Survived'])['Pclass'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[['Pclass','Survived']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass,Sex'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex','Pclass','Survived']].groupby(['Pclass','Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图和表中可以看出，总体上泰坦尼克号逃生是妇女优先，但是对于不同等级的船舱还是有一定的区别。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 年龄与存活与否的关系Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分别分析不同等级船舱和不同性别下的年龄分布和生存的关系：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2, figsize = (18,5))\n",
    "ax[0].set_yticks(range(0,110,10))\n",
    "sns.violinplot(\"Pclass\",\"Age\",hue=\"Survived\",data=train_data,split=True,ax=ax[0])\n",
    "ax[0].set_title('Pclass and Age vs Survived') \n",
    "\n",
    "ax[1].set_yticks(range(0,110,10))\n",
    "sns.violinplot(\"Sex\",\"Age\",hue=\"Survived\",data=train_data,split=True,ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析总体年龄分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "train_data['Age'].hist(bins=100)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Num')\n",
    " \n",
    "plt.subplot(122)\n",
    "train_data.boxplot(column='Age',showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同年龄下的生存和非生存的分布情况：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25ebae11dc8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 911x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train_data,hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade=True)\n",
    "facet.set(xlim=(0,train_data['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "不同年龄下的平均生存率：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age_int', ylabel='Survived'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# average survived passengers by age\n",
    "fig,axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "train_data['Age_int'] = train_data['Age'].astype(int)\n",
    "average_age = train_data[[\"Age_int\", \"Survived\"]].groupby(['Age_int'],as_index=False).mean()\n",
    "sns.barplot(x='Age_int',y='Survived',data=average_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    891.000000\n",
      "mean      29.659721\n",
      "std       13.735430\n",
      "min        0.420000\n",
      "25%       21.000000\n",
      "50%       28.000000\n",
      "75%       37.000000\n",
      "max       80.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Age'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本有891，平均年龄约为30岁，标准差13.5岁，最小年龄0.42，最大年龄80.\n",
    "按照年龄，将乘客划分为儿童、少年、成年、老年，分析四个群体的生还情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age_group\n",
      "(0, 12]      0.506173\n",
      "(12, 18]     0.466667\n",
      "(18, 65]     0.364512\n",
      "(65, 100]    0.125000\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "bins = [0, 12, 18, 65, 100]\n",
    "train_data['Age_group'] = pd.cut(train_data['Age'],bins)\n",
    "by_age = train_data.groupby('Age_group')['Survived'].mean()\n",
    "print(by_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# by_age.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 称呼与存活与否的关系 Name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过观察名字数据，我们可以看出其中包括对乘客的称呼，如：Mr、Miss、Mrs等，称呼信息包含了乘客的年龄、性别，同时也包含了入社会地位等的称呼，如：Dr，Lady，Major（少校），Master（硕士，主人，师傅）等的称呼。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Title'] = train_data['Name'].str.extract(' ([A-Za-z]+)\\.',expand=False)\n",
    "pd.crosstab(train_data['Title'],train_data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Title'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Title','Survived']].groupby(['Title']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时，对于名字，我们还可以观察名字长度和生存率之间存在关系的可能：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Name_length', ylabel='Survived'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "train_data['Name_length'] = train_data['Name'].apply(len)\n",
    "name_length = train_data[['Name_length','Survived']].groupby(['Name_length'], as_index=False).mean()\n",
    "sns.barplot(x='Name_length', y='Survived',data=name_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的图片可以看出，名字长度和生存与否确实也存在一定的相关性.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 有无兄弟姐妹和存活与否的关系 SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将数据分为有兄弟姐妹和没有兄弟姐妹的两组：\n",
    "sibsp_df = train_data[train_data['SibSp'] != 0]\n",
    "no_sibsp_df = train_data[train_data['SibSp'] == 0]\n",
    "\n",
    "plt.figure(figsize=(11,5))\n",
    "plt.subplot(121)\n",
    "sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct= '%1.1f%%')\n",
    "plt.xlabel('sibsp')\n",
    " \n",
    "plt.subplot(122)\n",
    "no_sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct= '%1.1f%%')\n",
    "plt.xlabel('no_sibsp')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.6 有无父母子女和存活与否的关系 Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parch_df = train_data[train_data['Parch'] != 0]  \n",
    "no_parch_df = train_data[train_data['Parch'] == 0]  \n",
    " \n",
    "plt.figure(figsize=(11,5))  \n",
    "plt.subplot(121)  \n",
    "parch_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct= '%1.2f%%')  \n",
    "plt.xlabel('parch')  \n",
    " \n",
    "plt.subplot(122)  \n",
    "no_parch_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct = '%1.2f%%')  \n",
    "plt.xlabel('no_parch') \n",
    " \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7 亲友的人数和存活与否的关系 SibSp & Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SibSp and Survived')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots(1,2,figsize=(15,5))\n",
    "train_data[['Parch','Survived']].groupby(['Parch']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('Parch and Survived')\n",
    "train_data[['SibSp','Survived']].groupby(['SibSp']).mean().plot.bar(ax=ax[1])\n",
    "ax[1].set_title('SibSp and Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Family_Size'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Family_Size'] = train_data['Parch'] + train_data['SibSp']+1\n",
    "train_data[['Family_Size','Survived']].groupby(['Family_Size']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图表中可以看出，若独自一人，那么其存活率比较低；但是如果亲友太多的话，存活率也会很低。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.8 票价分布和存活与否的关系 Fare\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先绘制票价的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "train_data['Fare'].hist(bins=70)\n",
    " \n",
    "train_data.boxplot(column='Fare', by='Pclass', showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    891.000000\n",
      "mean      32.204208\n",
      "std       49.693429\n",
      "min        0.000000\n",
      "25%        7.910400\n",
      "50%       14.454200\n",
      "75%       31.000000\n",
      "max      512.329200\n",
      "Name: Fare, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Fare'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制生存与否与票价均值和方差的关系：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fare_not_survived = train_data['Fare'][train_data['Survived'] == 0]\n",
    "fare_survived = train_data['Fare'][train_data['Survived'] == 1]\n",
    " \n",
    "average_fare = pd.DataFrame([fare_not_survived.mean(),fare_survived.mean()])\n",
    "std_fare = pd.DataFrame([fare_not_survived.std(),fare_survived.std()])\n",
    "average_fare.plot(yerr=std_fare,kind='bar',legend=False)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图表可知，票价与是否生还有一定的相关性，生还者的平均票价要大于未生还者的平均票价。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9 船舱类型和存活与否的关系 Cabin\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于船舱的缺失值确实太多，有效值仅仅有204个，很难分析出不同的船舱和存活的关系，所以在做特征工程的时候，可以直接将该组特征丢弃掉。 当然，这里我们也可以对其进行一下分析，对于缺失的数据都分为一类。 简单地将数据分为是否有Cabin记录作为特征，与生存与否进行分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Has_Cabin'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace missing values with \"U0\"\n",
    "train_data.loc[train_data.Cabin.isnull(),'Cabin'] = 'U0'\n",
    "train_data['Has_Cabin'] = train_data['Cabin'].apply(lambda x: 0 if x == 'U0' else 1)\n",
    "train_data[['Has_Cabin','Survived']].groupby(['Has_Cabin']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对不同类型的船舱进行分析：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CabinLetter'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create feature for the alphabetical part of the cabin number\n",
    "train_data['CabinLetter'] = train_data['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "# convert the distinct cabin letters with incremental integer values\n",
    "train_data['CabinLetter'] = pd.factorize(train_data['CabinLetter'])[0]\n",
    "train_data[['CabinLetter','Survived']].groupby(['CabinLetter']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见，不同的船舱生存率也有不同，但是差别不是很大。所以在处理中，我们可以直接将特征删除。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10 港口和存活与否的关系 Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "泰坦尼克号从英国的南安普顿港出发，途径法国瑟堡和爱尔兰昆士敦，那么在昆士敦之前上船的人，有可能在瑟堡或昆士敦下船，这些人将不会遇到海难。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Embarked and Survived')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Embarked',hue='Survived',data=train_data)\n",
    "plt.title('Embarked and Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked','Survived',data = train_data, size=3, aspect=2)\n",
    "plt.title('Embarked and Survived rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上可以看出，在不同的港口上船，生还率不同，C最高，Q次之，S最低。 以上为所给出的数据特征与生还与否的分析。 据了解，泰坦尼克号上共有2224名乘客。本训练数据只给出了891名乘客的信息，如果该数据集是从总共的2224人随机选出的，根据中心极限定理，该样本的数据量也足够大，那么我们的分析结果就具有代表性；但如果不是随机选取，那么我们的分析结果就可能不太靠谱了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.11 其他可能和存活与否有关系的特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于数据集中没有给出的特征信息，我们还可以联想其他可能会对模型产生影响的特征因素。如：乘客的国籍、乘客的身高、乘客的体重、乘客是否会游泳、乘客职业等等。\n",
    "另外还有数据集中没有分析的几个特征：Ticket（船票号）、Cabin（船舱号），这些因素的不同可能会影响乘客在船中的位置从而影响逃生的顺序。但是船舱号数据缺失，船票号类别大，难以分析规律，所以在后期模型融合的时候，将这些因素交由模型来决定其重要性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 变量转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "变量转换的目的是将数据转换为适用于模型使用的数据，不同模型接受不同类型的数据，Scikit-learn要求数据都是数字型numeric，所以我们要将一些非数字型的原始数据转换为数字型numeric。 所以下面对数据的转换进行介绍，以在进行特征工程的时候使用。 所有的数据可以分为两类：\n",
    "1. 定性（Qualitative）变量可以以某种方式，Age就是一个很好的例子。\n",
    "2. 定量（Quantitative）变量描述了物体的某一（不能被数学表示的）方面，Embarked就是一个例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**定性（Qualitative）转换：**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Dummy Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就是类别变量或者二元变量，当qualitative variable是一些频繁出现的几个独立变量时，Dummy Variables比较适用。我们以Embarked只包含三个值’S'，‘C'，’Q'，我们可以使用下面的代码将其转换为dummies："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S  C  Q\n",
       "0  1  0  0\n",
       "1  0  1  0\n",
       "2  1  0  0\n",
       "3  1  0  0\n",
       "4  1  0  0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 三个港口\n",
    "embark_dummies = pd.get_dummies(train_data['Embarked'])\n",
    "train_data = train_data.join(embark_dummies)\n",
    "train_data.drop(['Embarked'], axis=1, inplace=True)\n",
    "\n",
    "embark_dummies = train_data[['S','C','Q']]\n",
    "embark_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Factoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dummy不好处理Cabin（船舱号）这种标称属性，因为他出现的变量比较多。所以Pandas有一个方法叫做factorize()，它可以创建一些数字，来表示类别变量，对每一个类别映射一个ID，这种映射最后只生成一个特征，不像dummy那样生成多个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用“U0”来补充缺失值\n",
    "train_data['Cabin'][train_data.Cabin.isnull()] = 'U0'\n",
    "# 按照字母顺序为船舱数据创建特征\n",
    "train_data['CabinLetter'] = train_data['Cabin'].map(lambda x:re.compile('[a-zA-Z+]').search(x).group())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      U\n",
       "1      C\n",
       "2      U\n",
       "3      C\n",
       "4      U\n",
       "      ..\n",
       "886    U\n",
       "887    B\n",
       "888    U\n",
       "889    C\n",
       "890    U\n",
       "Name: CabinLetter, Length: 891, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "train_data['CabinLetter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>CabinLetter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>C85</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin  CabinLetter\n",
       "0    U0            0\n",
       "1   C85            1\n",
       "2    U0            0\n",
       "3  C123            1\n",
       "4    U0            0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用整数来映射船舱名称\n",
    "train_data['CabinLetter'] = pd.factorize(train_data['CabinLetter'])[0]\n",
    "train_data[['Cabin', 'CabinLetter']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>CabinLetter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin  CabinLetter\n",
       "0    U0            0\n",
       "1   C85            1\n",
       "2    U0            0\n",
       "3  C123            1\n",
       "4    U0            0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the distinct cabin letters with incremental integer values\n",
    "train_data['CabinLetter'] = pd.factorize(train_data['CabinLetter'])[0]\n",
    "\n",
    "train_data[['Cabin','CabinLetter']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**定量（Quantitative）转换：**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling可以将一个很大范围的数值映射到一个很小范围（通常是 -1到1，或者是0到1），很多情况下我们需要将数值做Scaling使其范围大小一样，否则大范围数特征将会有更高的权重。比如：Age的范围可能只是0-100，而income的范围可能是0-10000000，在某些对数组大小敏感的模型中会影响其结果。\n",
    "\n",
    "下面对Age进行Scaling："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "assert np.size(train_data['Age']) == 891\n",
    "# StandScaler可以将每个值减去平均值，然后进行单位化\n",
    "scaler = preprocessing.StandardScaler()\n",
    "train_data['Age_scaled'] = scaler.fit_transform(train_data['Age'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.667053314906868"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age_scaled'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4  Binnig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binning通过观察“邻居”（即周围的值）将连续数据离散化。存储的值被分布到一些“桶”或“箱”中，就像直方图的bin将数据划分成几块一样。\n",
    "下面的代码对Fare进行Binning。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      (-0.001, 7.854]\n",
      "1    (39.688, 512.329]\n",
      "2        (7.854, 10.5]\n",
      "3    (39.688, 512.329]\n",
      "4        (7.854, 10.5]\n",
      "Name: Fare_bin, dtype: category\n",
      "Categories (5, interval[float64]): [(-0.001, 7.854] < (7.854, 10.5] < (10.5, 21.679] < (21.679, 39.688] < (39.688, 512.329]]\n"
     ]
    }
   ],
   "source": [
    "# 将票价分成四个区间\n",
    "train_data['Fare_bin'] = pd.qcut(train_data['Fare'], 5)\n",
    "print(train_data['Fare_bin'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在将数据Binning化后，要么将数据factorize化，要么dummies化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qcut()创建一个新的变量来标志四个范围，但是我们不能使用字符串\n",
    "# 所以要么factorize化， 要么dummies化\n",
    "\n",
    "# factorize\n",
    "train_data['Fare_bin_id'] = pd.factorize(train_data['Fare_bin'])[0]\n",
    "\n",
    "# dummies\n",
    "fare_bin_dummies_df = pd.get_dummies(train_data['Fare_bin']).rename(columns=lambda x: 'Fare_' + str(x))\n",
    "trian_data = pd.concat([train_data,fare_bin_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Fare_(-0.001, 7.854]</th>\n",
       "      <th>Fare_(7.854, 10.5]</th>\n",
       "      <th>Fare_(10.5, 21.679]</th>\n",
       "      <th>Fare_(21.679, 39.688]</th>\n",
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       "      <td>38.000000</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.776511</td>\n",
       "      <td>(21.679, 39.688]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>25.344086</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.314374</td>\n",
       "      <td>(21.679, 39.688]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.266593</td>\n",
       "      <td>(21.679, 39.688]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170478</td>\n",
       "      <td>(-0.001, 7.854]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  25.344086   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare  ...  Q  S Age_scaled  \\\n",
       "0        1      0         A/5 21171   7.2500  ...  0  1  -0.557975   \n",
       "1        1      0          PC 17599  71.2833  ...  0  0   0.607550   \n",
       "2        0      0  STON/O2. 3101282   7.9250  ...  0  1  -0.266593   \n",
       "3        1      0            113803  53.1000  ...  0  1   0.389014   \n",
       "4        0      0            373450   8.0500  ...  0  1   0.389014   \n",
       "..     ...    ...               ...      ...  ... .. ..        ...   \n",
       "886      0      0            211536  13.0000  ...  0  1  -0.193748   \n",
       "887      0      0            112053  30.0000  ...  0  1  -0.776511   \n",
       "888      1      2        W./C. 6607  23.4500  ...  0  1  -0.314374   \n",
       "889      0      0            111369  30.0000  ...  0  0  -0.266593   \n",
       "890      0      0            370376   7.7500  ...  1  0   0.170478   \n",
       "\n",
       "              Fare_bin  Fare_bin_id  Fare_(-0.001, 7.854]  Fare_(7.854, 10.5]  \\\n",
       "0      (-0.001, 7.854]            0                     1                   0   \n",
       "1    (39.688, 512.329]            1                     0                   0   \n",
       "2        (7.854, 10.5]            2                     0                   1   \n",
       "3    (39.688, 512.329]            1                     0                   0   \n",
       "4        (7.854, 10.5]            2                     0                   1   \n",
       "..                 ...          ...                   ...                 ...   \n",
       "886     (10.5, 21.679]            3                     0                   0   \n",
       "887   (21.679, 39.688]            4                     0                   0   \n",
       "888   (21.679, 39.688]            4                     0                   0   \n",
       "889   (21.679, 39.688]            4                     0                   0   \n",
       "890    (-0.001, 7.854]            0                     1                   0   \n",
       "\n",
       "     Fare_(10.5, 21.679]  Fare_(21.679, 39.688]  Fare_(39.688, 512.329]  \n",
       "0                      0                      0                       0  \n",
       "1                      0                      0                       1  \n",
       "2                      0                      0                       0  \n",
       "3                      0                      0                       1  \n",
       "4                      0                      0                       0  \n",
       "..                   ...                    ...                     ...  \n",
       "886                    1                      0                       0  \n",
       "887                    0                      1                       0  \n",
       "888                    0                      1                       0  \n",
       "889                    0                      1                       0  \n",
       "890                    0                      0                       0  \n",
       "\n",
       "[891 rows x 29 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trian_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在进行特征工程的时候，我们不仅需要对训练数据进行处理，还需要同时将测试数据同训练数据一起处理，使得二者具有相同的数据类型和数据分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_org = pd.read_csv('../datasets/titannic/train.csv')\n",
    "test_df_org = pd.read_csv('../datasets/titannic/test.csv')\n",
    "test_df_org['Survived'] = 0\n",
    "combined_train_test = train_df_org.append(test_df_org)   #891+418=1309rows, 12columns\n",
    "PassengerId = test_df_org['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据进行特征工程，也就是从各项参数中提取出对输出结果有或大或小的影响的特征，将这些特征作为训练模型的依据。一般来说，我们会先从含有缺失值的特征开始。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为“Embarked”项的缺失值不多，所以这里我们以众数来填充："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Embarked'].fillna(combined_train_test['Embarked'].mode().iloc[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于三种不同的港口，由上面介绍的数值转换，我们知道可以有两种特征处理方式；dummy和factorizing。因为只有三个港口，所以我们可以直接用dummy来处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#为了后面的特征分析，这里我们将Embarked特征进行factorizing\n",
    "combined_train_test['Embarked'] = pd.factorize(combined_train_test['Embarked'])[0]\n",
    " \n",
    "#使用pd.get_dummies获取one-hot编码\n",
    "emb_dummies_df = pd.get_dummies(combined_train_test['Embarked'],prefix=combined_train_test[['Embarked']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, emb_dummies_df], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Sex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对Sex也进行one-hot编码，也就是dummy处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了后面的特征分析，这里我们也将Sex特征进行factorizing\n",
    "combined_train_test['Sex'] = pd.factorize(combined_train_test['Sex'])[0]\n",
    " \n",
    "sex_dummies_df = pd.get_dummies(combined_train_test['Sex'],prefix=combined_train_test[['Sex']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test,sex_dummies_df],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先从名字中提取各种称呼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what is each person's title?\n",
    "combined_train_test['Title'] = combined_train_test['Name'].map(lambda x: re.compile(\",(.*?)\\.\").findall(x)[0])\n",
    "combined_train_test['Title'] = combined_train_test['Title'].apply(lambda x:x.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管提取的Title两句话得到的效果是一样的，但是如果用\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管提取的Title两句话得到的效果是一样的，但是如果用\n",
    "```python \n",
    "combined_train_test['Title'] = combined_train_test['Name'].map(lambda x: re.compile(\",(.*?)\\.\").findall(x)[0])\n",
    "```\n",
    "下面的语句执行后有问题。\n",
    "\n",
    "将各式称呼进行统一化处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_Dict = {}\n",
    "title_Dict.update(dict.fromkeys(['Capt','Col','Major','Dr','Rev'],'Officer'))\n",
    "title_Dict.update(dict.fromkeys(['Don','Sir','the Countess','Dona','Lady'],'Royalty'))\n",
    "title_Dict.update(dict.fromkeys(['Mme','Ms','Mrs'],'Mrs'))\n",
    "title_Dict.update(dict.fromkeys(['Male','Miss'],'Miss'))\n",
    "title_Dict.update(dict.fromkeys(['Mr'],'Mr'))\n",
    "title_Dict.update(dict.fromkeys(['Master','Jonkheer'],'Master'))\n",
    " \n",
    "combined_train_test['Title'] = combined_train_test['Title'].map(title_Dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用dummy对不同的称呼进行分列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#为了后面的特征分析，这里我们也将Title特征进行factorizing\n",
    "combined_train_test['Title'] = pd.factorize(combined_train_test['Title'])[0]\n",
    "title_dummies_df = pd.get_dummies(combined_train_test['Title'],prefix=combined_train_test[['Title']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test,title_dummies_df],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "增加名字长度的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Name_length'] = combined_train_test['Name'].apply(len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由前面分析可以知道，Fare项在测试数据中缺少一个值，所以需要对该值进行填充。我们按照一二三等舱各自的均价来填充：\n",
    "\n",
    "下面transform将函数np.mean应用到各个group中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Fare'] = combined_train_test[['Fare']].fillna(combined_train_test.groupby('Pclass').transform(np.mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过对Ticket数据的分析，我们可以看到部分票号数据有重复，同时结合亲属人数及名字的数据，和票价船舱等级对比，我们可以知道购买的票中有家庭票和团体票，所以我们需要将团体票的票价分配到每个人的头上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Group_Ticket'] = combined_train_test['Fare'].groupby(by=combined_train_test['Ticket']).transform('count')\n",
    "combined_train_test['Fare'] = combined_train_test['Fare']/combined_train_test['Group_Ticket']\n",
    "combined_train_test.drop(['Group_Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用binning给票价分等级：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Fare_bin'] = pd.qcut(combined_train_test['Fare'],5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于5个等级的票价我们可以继续使用dummy为票价等价分列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Fare_bin'] = pd.qcut(combined_train_test['Fare'],5)\n",
    " \n",
    "combined_train_test['Fare_bin_id'] = pd.factorize(combined_train_test['Fare_bin'])[0]\n",
    " \n",
    "fare_bin_dummies_df = pd.get_dummies(combined_train_test['Fare_bin_id']).rename(columns=lambda x: 'Fare_' + str(x))\n",
    "combined_train_test = pd.concat([combined_train_test,fare_bin_dummies_df],axis=1)\n",
    "combined_train_test.drop(['Fare_bin'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pclass这一项，其实已经可以不用继续处理了，我们只需将其转换为dummy形式即可。 但是为了更好的分析，我们这里假设对于不同等级的船舱，各船舱内部的票价也说明了各等级舱的位置，那么也就很有可能与逃生的顺序有关系。所以这里分析出每等舱里的高价和低价位。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass\n",
      "1    33.910500\n",
      "2    11.411010\n",
      "3     7.337571\n",
      "Name: Fare, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    " \n",
    "#建立Pclass Fare Category\n",
    "def pclass_fare_category(df,pclass1_mean_fare,pclass2_mean_fare,pclass3_mean_fare):\n",
    "    if df['Pclass'] == 1:\n",
    "        if df['Fare'] <= pclass1_mean_fare:\n",
    "            return 'Pclass1_Low'\n",
    "        else:\n",
    "            return 'Pclass1_High'\n",
    "    elif df['Pclass'] == 2:\n",
    "        if df['Fare'] <= pclass2_mean_fare:\n",
    "            return 'Pclass2_Low'\n",
    "        else:\n",
    "            return 'Pclass2_High'\n",
    "    elif df['Pclass'] == 3:\n",
    "        if df['Fare'] <= pclass3_mean_fare:\n",
    "            return 'Pclass3_Low'\n",
    "        else:\n",
    "            return 'Pclass3_High'\n",
    " \n",
    "Pclass1_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get(1)\n",
    "Pclass2_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get(2)\n",
    "Pclass3_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get(3)\n",
    " \n",
    "#建立Pclass_Fare Category\n",
    "combined_train_test['Pclass_Fare_Category'] = combined_train_test.apply(pclass_fare_category,args=(\n",
    "        Pclass1_mean_fare,Pclass2_mean_fare,Pclass3_mean_fare),axis=1)\n",
    "pclass_level = LabelEncoder()\n",
    " \n",
    "#给每一项添加标签\n",
    "pclass_level.fit(np.array(['Pclass1_Low','Pclass1_High','Pclass2_Low','Pclass2_High','Pclass3_Low','Pclass3_High']))\n",
    " \n",
    "#转换成数值\n",
    "combined_train_test['Pclass_Fare_Category'] = pclass_level.transform(combined_train_test['Pclass_Fare_Category'])\n",
    " \n",
    "# dummy 转换\n",
    "pclass_dummies_df = pd.get_dummies(combined_train_test['Pclass_Fare_Category']).rename(columns=lambda x: 'Pclass_' + str(x))\n",
    "combined_train_test = pd.concat([combined_train_test,pclass_dummies_df],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 同时，我们将Pclass特征factorize化：\n",
    "combined_train_test['Pclass'] = pd.factorize(combined_train_test['Pclass'])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6 Parch and SibSp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由前面的分析，我们可以知道，亲友的数量没有或者太多会影响到Survived。所以将二者合并为FamliySize这一组合项，同时也保留这两项。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def family_size_category(family_size):\n",
    "    if family_size <= 1:\n",
    "        return 'Single'\n",
    "    elif family_size <= 4:\n",
    "        return 'Small_Family'\n",
    "    else:\n",
    "        return 'Large_Family'\n",
    "\n",
    "combined_train_test['Family_Size'] = combined_train_test['Parch'] + combined_train_test['SibSp'] + 1\n",
    "combined_train_test['Family_Size_Category'] = combined_train_test['Family_Size'].map(family_size_category)\n",
    "\n",
    "le_family = LabelEncoder()\n",
    "le_family.fit(np.array(['Single', 'Small_Family', 'Large_Family']))\n",
    "combined_train_test['Family_Size_Category'] = le_family.transform(combined_train_test['Family_Size_Category'])\n",
    "\n",
    "family_size_dummies_df = pd.get_dummies(combined_train_test['Family_Size_Category'],\n",
    "                                        prefix=combined_train_test[['Family_Size_Category']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, family_size_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.7 Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为Age项的缺失值较多，所以不能直接填充age的众数或者平均数。\n",
    "\n",
    "常见的有两种对年龄的填充方式：一种是根据Title中的称呼，如Mr，Master、Miss等称呼不同类别的人的平均年龄来填充；一种是综合几项如Sex、Title、Pclass等其他没有缺失值的项，使用机器学习算法来预测Age。\n",
    "\n",
    "这里我们使用后者来处理。以Age为目标值，将Age完整的项作为训练集，将Age缺失的项作为测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Title</th>\n",
       "      <th>Name_length</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Family_Size_Category</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare_bin_id</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Embarked  Sex  Title  Name_length  Family_Size  Family_Size_Category  \\\n",
       "5   NaN         2    0      0           16            1                     1   \n",
       "17  NaN         0    0      0           28            1                     1   \n",
       "19  NaN         1    1      1           23            1                     1   \n",
       "26  NaN         1    0      0           23            1                     1   \n",
       "28  NaN         2    1      2           29            1                     1   \n",
       "\n",
       "       Fare  Fare_bin_id  Pclass  \n",
       "5    8.4583            2       0  \n",
       "17  13.0000            3       2  \n",
       "19   7.2250            4       0  \n",
       "26   7.2250            4       0  \n",
       "28   7.8792            0       0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_age_df = pd.DataFrame(combined_train_test[\n",
    "    ['Age', 'Embarked', 'Sex', 'Title', 'Name_length', 'Family_Size', 'Family_Size_Category','Fare', 'Fare_bin_id', 'Pclass']])\n",
    "\n",
    "missing_age_train = missing_age_df[missing_age_df['Age'].notnull()]\n",
    "missing_age_test = missing_age_df[missing_age_df['Age'].isnull()]\n",
    "\n",
    "missing_age_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "建立Age的预测模型，我们可以多模型预测，然后再做模型的融合，提高预测的精度。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def fill_missing_age(missing_age_train, missing_age_test):\n",
    "    missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\n",
    "    missing_age_Y_train = missing_age_train['Age']\n",
    "    missing_age_X_test = missing_age_test.drop(['Age'], axis=1)\n",
    "\n",
    "    # model 1  gbm\n",
    "    gbm_reg = GradientBoostingRegressor(random_state=42)\n",
    "    gbm_reg_param_grid = {'n_estimators': [2000], 'max_depth': [4], 'learning_rate': [0.01], 'max_features': [3]}\n",
    "    gbm_reg_grid = model_selection.GridSearchCV(gbm_reg, gbm_reg_param_grid, cv=10, n_jobs=25, verbose=1, scoring='neg_mean_squared_error')\n",
    "    gbm_reg_grid.fit(missing_age_X_train, missing_age_Y_train)\n",
    "    print('Age feature Best GB Params:' + str(gbm_reg_grid.best_params_))\n",
    "    print('Age feature Best GB Score:' + str(gbm_reg_grid.best_score_))\n",
    "    print('GB Train Error for \"Age\" Feature Regressor:' + str(gbm_reg_grid.score(missing_age_X_train, missing_age_Y_train)))\n",
    "    missing_age_test.loc[:, 'Age_GB'] = gbm_reg_grid.predict(missing_age_X_test)\n",
    "    print(missing_age_test['Age_GB'][:4])\n",
    "\n",
    "    # model 2 rf\n",
    "    rf_reg = RandomForestRegressor()\n",
    "    rf_reg_param_grid = {'n_estimators': [200], 'max_depth': [5], 'random_state': [0]}\n",
    "    rf_reg_grid = model_selection.GridSearchCV(rf_reg, rf_reg_param_grid, cv=10, n_jobs=25, verbose=1, scoring='neg_mean_squared_error')\n",
    "    rf_reg_grid.fit(missing_age_X_train, missing_age_Y_train)\n",
    "    print('Age feature Best RF Params:' + str(rf_reg_grid.best_params_))\n",
    "    print('Age feature Best RF Score:' + str(rf_reg_grid.best_score_))\n",
    "    print('RF Train Error for \"Age\" Feature Regressor' + str(rf_reg_grid.score(missing_age_X_train, missing_age_Y_train)))\n",
    "    missing_age_test.loc[:, 'Age_RF'] = rf_reg_grid.predict(missing_age_X_test)\n",
    "    print(missing_age_test['Age_RF'][:4])\n",
    "\n",
    "    # two models merge\n",
    "    print('shape1', missing_age_test['Age'].shape, missing_age_test[['Age_GB', 'Age_RF']].mode(axis=1).shape)\n",
    "    # missing_age_test['Age'] = missing_age_test[['Age_GB', 'Age_LR']].mode(axis=1)\n",
    "\n",
    "    missing_age_test.loc[:, 'Age'] = np.mean([missing_age_test['Age_GB'], missing_age_test['Age_RF']])\n",
    "    print(missing_age_test['Age'][:4])\n",
    "\n",
    "    missing_age_test.drop(['Age_GB', 'Age_RF'], axis=1, inplace=True)\n",
    "\n",
    "    return missing_age_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "Age feature Best GB Params:{'learning_rate': 0.01, 'max_depth': 4, 'max_features': 3, 'n_estimators': 2000}\n",
      "Age feature Best GB Score:-128.38286366239385\n",
      "GB Train Error for \"Age\" Feature Regressor:-65.2562037120689\n",
      "5     37.508266\n",
      "17    31.580052\n",
      "19    34.597808\n",
      "26    29.076996\n",
      "Name: Age_GB, dtype: float64\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "Age feature Best RF Params:{'max_depth': 5, 'n_estimators': 200, 'random_state': 0}\n",
      "Age feature Best RF Score:-119.64194051962507\n",
      "RF Train Error for \"Age\" Feature Regressor-96.82296812792812\n",
      "5     33.513123\n",
      "17    33.098071\n",
      "19    34.853983\n",
      "26    28.148613\n",
      "Name: Age_RF, dtype: float64\n",
      "shape1 (263,) (263, 2)\n",
      "5     29.97686\n",
      "17    29.97686\n",
      "19    29.97686\n",
      "26    29.97686\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 利用融合模型预测的结果填充Age的缺失值：\n",
    "\n",
    "combined_train_test.loc[(combined_train_test.Age.isnull()), 'Age'] = fill_missing_age(missing_age_train, missing_age_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Title</th>\n",
       "      <th>Name_length</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Family_Size_Category</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare_bin_id</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.97686</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>29.97686</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29.97686</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>29.97686</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.97686</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  Embarked  Sex  Title  Name_length  Family_Size  \\\n",
       "5   29.97686         2    0      0           16            1   \n",
       "17  29.97686         0    0      0           28            1   \n",
       "19  29.97686         1    1      1           23            1   \n",
       "26  29.97686         1    0      0           23            1   \n",
       "28  29.97686         2    1      2           29            1   \n",
       "\n",
       "    Family_Size_Category     Fare  Fare_bin_id  Pclass  \n",
       "5                      1   8.4583            2       0  \n",
       "17                     1  13.0000            3       2  \n",
       "19                     1   7.2250            4       0  \n",
       "26                     1   7.2250            4       0  \n",
       "28                     1   7.8792            0       0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_age_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.8 Ticket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察Ticket的值，我们可以看到，Ticket有字母和数字之分，而对于不同的字母，可能在很大程度上就意味着船舱等级或者不同船舱的位置，也会对Survived产生一定的影响，所以我们将Ticket中的字母分开，为数字的部分则分为一类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Ticket_Letter'] = combined_train_test['Ticket'].str.split().str[0]\n",
    "combined_train_test['Ticket_Letter'] = combined_train_test['Ticket_Letter'].apply(lambda x: 'U0' if x.isnumeric() else x)\n",
    "\n",
    "# 如果要提取数字信息，则也可以这样做，现在我们对数字票单纯地分为一类。\n",
    "# combined_train_test['Ticket_Number'] = combined_train_test['Ticket'].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
    "# combined_train_test['Ticket_Number'].fillna(0, inplace=True)\n",
    "\n",
    "# 将 Ticket_Letter factorize\n",
    "combined_train_test['Ticket_Letter'] = pd.factorize(combined_train_test['Ticket_Letter'])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.9 Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为Cabin项的缺失值确实太多了，我们很难对其进行分析，或者预测。所以这里我们可以直接将Cabin这一项特征去除。但通过上面的分析，可以知道，该特征信息的有无也与生存率有一定的关系，所以这里我们暂时保留该特征，并将其分为有和无两类。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test.loc[combined_train_test.Cabin.isnull(), 'Cabin'] = 'U0'\n",
    "combined_train_test['Cabin'] = combined_train_test['Cabin'].apply(lambda x: 0 if x == 'U0' else 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.10 特征间相关性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们挑选一些主要的特征，生成特征之间的关联图，查看特征与特征之间的相关性：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "Correlation = pd.DataFrame(combined_train_test[['Embarked','Sex','Title','Name_length','Family_Size',\n",
    "                                                'Family_Size_Category','Fare','Fare_bin_id','Pclass',\n",
    "                                                'Pclass_Fare_Category','Age','Ticket_Letter','Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Pearson Correaltion of Feature'}>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correaltion of Feature',y=1.05,size=15)\n",
    "sns.heatmap(Correlation.astype(float).corr(),linewidths=0.1,vmax=1.0,square=True,cmap=colormap,linecolor='white',annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.11 特征之间的数据分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x25ebcd69808>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 738.2x691.2 with 72 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(combined_train_test[[u'Survived',u'Pclass',u'Sex',u'Age',u'Fare',u'Embarked',\n",
    "                                      u'Family_Size',u'Title',u'Ticket_Letter']],hue='Survived',\n",
    "                                      palette = 'seismic',size=1.2,diag_kind ='kde',diag_kws=\n",
    "                                      dict(shade=True),plot_kws=dict(s=10))\n",
    "g.set(xticklabels=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.12 输入模型前的一些处理："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.12.1 一些数据的正则化 这里我们将Age和fare进行正则化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "scale_age_fare = preprocessing.StandardScaler().fit(combined_train_test[['Age','Fare','Name_length']])\n",
    "combined_train_test[['Age','Fare','Name_length']] = scale_age_fare.transform(combined_train_test[['Age','Fare','Name_length']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.12.2 弃掉无用特征\n",
    "\n",
    "对于上面的特征工程中，我们从一些原始的特征中提取出了很多要融合到模型中的特征，但是我们需要剔除那些原本的我们用不到的或者非数值特征： 首先对我们的数据先进行一下备份，以便后期的再次分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data_backup = combined_train_test\n",
    "\n",
    "combined_train_test.drop(['PassengerId','Embarked','Sex','Name','Fare_bin_id','Pclass_Fare_Category',                          'Parch','SibSp','Family_Size_Category','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.12.3 将训练数据和测试数据分开："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = combined_train_test[:891]\n",
    "test_data = combined_train_test[891:]\n",
    " \n",
    "titanic_train_data_X = train_data.drop(['Survived'],axis=1)\n",
    "titanic_train_data_Y = train_data['Survived']\n",
    "titanic_test_data_X = test_data.drop(['Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 34)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train_data_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 34 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Pclass                  891 non-null    int64  \n",
      " 1   Age                     891 non-null    float64\n",
      " 2   Fare                    891 non-null    float64\n",
      " 3   Cabin                   891 non-null    int64  \n",
      " 4   Embarked_0              891 non-null    uint8  \n",
      " 5   Embarked_1              891 non-null    uint8  \n",
      " 6   Embarked_2              891 non-null    uint8  \n",
      " 7   Sex_0                   891 non-null    uint8  \n",
      " 8   Sex_1                   891 non-null    uint8  \n",
      " 9   Title                   891 non-null    int64  \n",
      " 10  Title_-1                891 non-null    uint8  \n",
      " 11  Title_0                 891 non-null    uint8  \n",
      " 12  Title_1                 891 non-null    uint8  \n",
      " 13  Title_2                 891 non-null    uint8  \n",
      " 14  Title_3                 891 non-null    uint8  \n",
      " 15  Title_4                 891 non-null    uint8  \n",
      " 16  Title_5                 891 non-null    uint8  \n",
      " 17  Name_length             891 non-null    float64\n",
      " 18  Fare_0                  891 non-null    uint8  \n",
      " 19  Fare_1                  891 non-null    uint8  \n",
      " 20  Fare_2                  891 non-null    uint8  \n",
      " 21  Fare_3                  891 non-null    uint8  \n",
      " 22  Fare_4                  891 non-null    uint8  \n",
      " 23  Pclass_0                891 non-null    uint8  \n",
      " 24  Pclass_1                891 non-null    uint8  \n",
      " 25  Pclass_2                891 non-null    uint8  \n",
      " 26  Pclass_3                891 non-null    uint8  \n",
      " 27  Pclass_4                891 non-null    uint8  \n",
      " 28  Pclass_5                891 non-null    uint8  \n",
      " 29  Family_Size             891 non-null    int64  \n",
      " 30  Family_Size_Category_0  891 non-null    uint8  \n",
      " 31  Family_Size_Category_1  891 non-null    uint8  \n",
      " 32  Family_Size_Category_2  891 non-null    uint8  \n",
      " 33  Ticket_Letter           891 non-null    int64  \n",
      "dtypes: float64(3), int64(5), uint8(26)\n",
      "memory usage: 85.3 KB\n"
     ]
    }
   ],
   "source": [
    "titanic_train_data_X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 模型融合及测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型融合的过程需要分几步来进行。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 利用不同的模型来对特征进行筛选，选出较为重要的特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    " \n",
    "def get_top_n_features(titanic_train_data_X,titanic_train_data_Y,top_n_features):\n",
    "    \n",
    "    #randomforest\n",
    "    rf_est = RandomForestClassifier(random_state=0)\n",
    "    rf_param_grid = {'n_estimators':[500],'min_samples_split':[2,3],'max_depth':[20]}\n",
    "    rf_grid = model_selection.GridSearchCV(rf_est,rf_param_grid,n_jobs=25,cv=10,verbose=1)\n",
    "    rf_grid.fit(titanic_train_data_X,titanic_train_data_Y)\n",
    "    print('Top N Features Best RF Params:' + str(rf_grid.best_params_))\n",
    "    print('Top N Features Best RF Score:' + str(rf_grid.best_score_))\n",
    "    print('Top N Features RF Train Score:' + str(rf_grid.score(titanic_train_data_X,titanic_train_data_Y)))\n",
    "    feature_imp_sorted_rf = pd.DataFrame({'feature':list(titanic_train_data_X),\n",
    "                                          'importance':rf_grid.best_estimator_.feature_importances_}).sort_values('importance',ascending=False)\n",
    "    features_top_n_rf = feature_imp_sorted_rf.head(top_n_features)['feature']\n",
    "    print('Sample 10 Feeatures from RF Classifier')\n",
    "    print(str(features_top_n_rf[:10]))\n",
    "    \n",
    "    #AdaBoost\n",
    "    ada_est = AdaBoostClassifier(random_state=0)\n",
    "    ada_param_grid = {'n_estimators':[500],'learning_rate':[0.01,0.1]}\n",
    "    ada_grid = model_selection.GridSearchCV(ada_est,ada_param_grid,n_jobs=25,cv=10,verbose=1)\n",
    "    ada_grid.fit(titanic_train_data_X,titanic_train_data_Y)\n",
    "    print('Top N Features Best Ada Params:' + str(ada_grid.best_params_))\n",
    "    print('Top N Features Best Ada Score:' + str(ada_grid.best_score_))\n",
    "    print('Top N Features Ada Train Score:' + str(ada_grid.score(titanic_train_data_X,titanic_train_data_Y)))\n",
    "    feature_imp_sorted_ada = pd.DataFrame({'feature':list(titanic_train_data_X),\n",
    "                                           'importance':ada_grid.best_estimator_.feature_importances_}).sort_values('importance',ascending=False)\n",
    "    features_top_n_ada = feature_imp_sorted_ada.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from Ada Classifier:')\n",
    "    print(str(features_top_n_ada[:10]))\n",
    "    \n",
    "    #ExtraTree\n",
    "    et_est = ExtraTreesClassifier(random_state=0)\n",
    "    et_param_grid = {'n_estimators':[500],'min_samples_split':[3,4],'max_depth':[20]}\n",
    "    et_grid = model_selection.GridSearchCV(et_est,et_param_grid,n_jobs=25,cv=10,verbose=1)\n",
    "    et_grid.fit(titanic_train_data_X,titanic_train_data_Y)\n",
    "    print('Top N Features Best ET Params:' + str(et_grid.best_params_))\n",
    "    print('Top N Features Best DT Score:' + str(et_grid.best_score_))\n",
    "    print('Top N Features ET Train Score:' + str(et_grid.score(titanic_train_data_X,titanic_train_data_Y)))\n",
    "    feature_imp_sorted_et = pd.DataFrame({'feature':list(titanic_train_data_X),\n",
    "                                          'importance':et_grid.best_estimator_.feature_importances_}).sort_values('importance',ascending=False)\n",
    "    features_top_n_et = feature_imp_sorted_et.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from ET Classifier:')\n",
    "    print(str(features_top_n_et[:10]))\n",
    "    \n",
    "    # GradientBoosting\n",
    "    gb_est = GradientBoostingClassifier(random_state=0)\n",
    "    gb_param_grid = {'n_estimators':[500],'learning_rate':[0.01,0.1],'max_depth':[20]}\n",
    "    gb_grid = model_selection.GridSearchCV(gb_est,gb_param_grid,n_jobs=25,cv=10,verbose=1)\n",
    "    gb_grid.fit(titanic_train_data_X,titanic_train_data_Y)\n",
    "    print('Top N Features Best GB Params:' + str(gb_grid.best_params_))\n",
    "    print('Top N Features Best GB Score:' + str(gb_grid.best_score_))\n",
    "    print('Top N Features GB Train Score:' + str(gb_grid.score(titanic_train_data_X,titanic_train_data_Y)))\n",
    "    feature_imp_sorted_gb = pd.DataFrame({'feature':list(titanic_train_data_X),\n",
    "                                          'importance':gb_grid.best_estimator_.feature_importances_}).sort_values('importance',ascending=False)\n",
    "    features_top_n_gb = feature_imp_sorted_gb.head(top_n_features)['feature']\n",
    "    print('Sample 10 Feature from GB Classifier:')\n",
    "    print(str(features_top_n_gb[:10]))\n",
    "    \n",
    "    # DecisionTree\n",
    "    dt_est = DecisionTreeClassifier(random_state=0)\n",
    "    dt_param_grid = {'min_samples_split':[2,4],'max_depth':[20]}\n",
    "    dt_grid = model_selection.GridSearchCV(dt_est,dt_param_grid,n_jobs=25,cv=10,verbose=1)\n",
    "    dt_grid.fit(titanic_train_data_X,titanic_train_data_Y)\n",
    "    print('Top N Features Bset DT Params:' + str(dt_grid.best_params_))\n",
    "    print('Top N Features Best DT Score:' + str(dt_grid.best_score_))\n",
    "    print('Top N Features DT Train Score:' + str(dt_grid.score(titanic_train_data_X,titanic_train_data_Y)))\n",
    "    feature_imp_sorted_dt = pd.DataFrame({'feature':list(titanic_train_data_X),\n",
    "                                          'importance':dt_grid.best_estimator_.feature_importances_}).sort_values('importance',ascending=False)\n",
    "    features_top_n_dt = feature_imp_sorted_dt.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from DT Classifier:')\n",
    "    print(str(features_top_n_dt[:10]))\n",
    "    \n",
    "    # merge the three models\n",
    "    features_top_n = pd.concat([features_top_n_rf,features_top_n_ada,features_top_n_et,features_top_n_gb,features_top_n_dt],\n",
    "                              ignore_index=True).drop_duplicates()\n",
    "    features_importance = pd.concat([feature_imp_sorted_rf,feature_imp_sorted_ada,feature_imp_sorted_et,\n",
    "                                     feature_imp_sorted_gb,feature_imp_sorted_dt],ignore_index=True)\n",
    "    \n",
    "    return features_top_n,features_importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 依据我们筛选出的特征构建训练集和测试集\n",
    "\n",
    "但如果在进行特征工程的过程中，产生了大量的特征，而特征与特征之间会存在一定的相关性。太多的特征一方面会影响训练的速度，另一方面也可能会使得模型过拟合。所以在特征太多的情况下，我们可以利用不同的模型对特征进行筛选，选取我们想要的前n个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Best RF Params:{'max_depth': 20, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "Top N Features Best RF Score:0.8271785268414481\n",
      "Top N Features RF Train Score:0.9764309764309764\n",
      "Sample 10 Feeatures from RF Classifier\n",
      "1               Age\n",
      "17      Name_length\n",
      "2              Fare\n",
      "8             Sex_1\n",
      "9             Title\n",
      "11          Title_0\n",
      "7             Sex_0\n",
      "29      Family_Size\n",
      "0            Pclass\n",
      "33    Ticket_Letter\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Best Ada Params:{'learning_rate': 0.01, 'n_estimators': 500}\n",
      "Top N Features Best Ada Score:0.8181897627965042\n",
      "Top N Features Ada Train Score:0.8204264870931538\n",
      "Sample 10 Features from Ada Classifier:\n",
      "11                   Title_0\n",
      "2                       Fare\n",
      "30    Family_Size_Category_0\n",
      "29               Family_Size\n",
      "7                      Sex_0\n",
      "0                     Pclass\n",
      "3                      Cabin\n",
      "8                      Sex_1\n",
      "17               Name_length\n",
      "1                        Age\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Best ET Params:{'max_depth': 20, 'min_samples_split': 4, 'n_estimators': 500}\n",
      "Top N Features Best DT Score:0.8237952559300874\n",
      "Top N Features ET Train Score:0.9708193041526375\n",
      "Sample 10 Features from ET Classifier:\n",
      "11          Title_0\n",
      "7             Sex_0\n",
      "8             Sex_1\n",
      "17      Name_length\n",
      "1               Age\n",
      "2              Fare\n",
      "3             Cabin\n",
      "9             Title\n",
      "33    Ticket_Letter\n",
      "13          Title_2\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Best GB Params:{'learning_rate': 0.1, 'max_depth': 20, 'n_estimators': 500}\n",
      "Top N Features Best GB Score:0.7835205992509363\n",
      "Top N Features GB Train Score:0.9966329966329966\n",
      "Sample 10 Feature from GB Classifier:\n",
      "11                   Title_0\n",
      "1                        Age\n",
      "2                       Fare\n",
      "17               Name_length\n",
      "30    Family_Size_Category_0\n",
      "29               Family_Size\n",
      "28                  Pclass_5\n",
      "9                      Title\n",
      "0                     Pclass\n",
      "33             Ticket_Letter\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Bset DT Params:{'max_depth': 20, 'min_samples_split': 4}\n",
      "Top N Features Best DT Score:0.7823220973782771\n",
      "Top N Features DT Train Score:0.9607182940516273\n",
      "Sample 10 Features from DT Classifier:\n",
      "11                   Title_0\n",
      "1                        Age\n",
      "2                       Fare\n",
      "17               Name_length\n",
      "30    Family_Size_Category_0\n",
      "16                   Title_5\n",
      "28                  Pclass_5\n",
      "0                     Pclass\n",
      "33             Ticket_Letter\n",
      "29               Family_Size\n",
      "Name: feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "feature_to_pick = 30\n",
    "feature_top_n,feature_importance = get_top_n_features(titanic_train_data_X,titanic_train_data_Y,feature_to_pick)\n",
    "titanic_train_data_X = pd.DataFrame(titanic_train_data_X[feature_top_n])\n",
    "titanic_test_data_X = pd.DataFrame(titanic_test_data_X[feature_top_n])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用视图可视化不同算法筛选的特征排序：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_feature_imp = feature_importance[:10]\n",
    "Ada_feature_imp = feature_importance[32:32+10].reset_index(drop=True)\n",
    "\n",
    "# make importances relative to max importance\n",
    "rf_feature_importance = 100.0 * (rf_feature_imp['importance'] / rf_feature_imp['importance'].max())\n",
    "Ada_feature_importance = 100.0 * (Ada_feature_imp['importance'] / Ada_feature_imp['importance'].max())\n",
    "\n",
    "# Get the indexes of all features over the importance threshold\n",
    "rf_important_idx = np.where(rf_feature_importance)[0]\n",
    "Ada_important_idx = np.where(Ada_feature_importance)[0]\n",
    "\n",
    "# Adapted from http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html\n",
    "pos = np.arange(rf_important_idx.shape[0]) + .5\n",
    "\n",
    "plt.figure(1, figsize = (18, 8))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.barh(pos, rf_feature_importance[rf_important_idx][::-1])\n",
    "plt.yticks(pos, rf_feature_imp['feature'][::-1])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('RandomForest Feature Importance')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.barh(pos, Ada_feature_importance[Ada_important_idx][::-1])\n",
    "plt.yticks(pos, Ada_feature_imp['feature'][::-1])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('AdaBoost Feature Importance')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 模型融合（Model Ensemble）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常见的模型融合方法有：Bagging、Boosting、Stacking、Blending。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3.1 Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging将多个模型，也就是基学习器的预测结果进行简单的加权平均或者投票。它的好处是可以并行地训练基学习器。Random Forest就用到了Bagging的思想。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3.2Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boosting的思想有点像知错能改，每个基学习器是在上一个基学习器学习的基础上，对上一个基学习器的错误进行弥补。我们将会用到的AdaBoost，Gradient Boost就用到了这种思想。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3.3. Stacking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stacking是用新的次学习器去学习如何组合上一层的基学习器。如果把Bagging看作是多个基分类器的线性组合，那么Stacking就是多个基分类器的非线性组合。Stacking可以将学习器一层一层地堆砌起来，形成一个网状的结构。 相比来说Stacking的融合框架相对前面二者来说在精度上确实有一定的提升，所以在下面的模型融合上，我们也使用Stacking方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3.4 Blending\n",
    "Blending和Stacking很相似，但同时它可以防止信息泄露的问题。\n",
    "\n",
    "Stacking框架融合：这里我们使用了两层的模型融合\n",
    "\n",
    "Level 1使用了：Random Forest、AdaBoost、ExtraTrees、GBDT、Decision Tree、KNN、SVM，一共7个模型\n",
    "\n",
    "Level 2使用了XGBoost，使用第一层预测的结果作为特征对最终的结果进行预测。\n",
    "\n",
    "Level 1：\n",
    "\n",
    "Stacking框架是堆叠使用基础分类器的预测作为对二级模型的训练的输入。然而，我们不能简单地在全部训练数据上训练基本模型，产生预测，输出用于第二层的训练。如果我们在Train Data上训练，然后在Train Data上预测，就会造成标签。为了避免标签，我们需要对每个基学习器使用K-fold，将Kge模型对Valid Set的预测结果拼起来，作为下一层学习器的输入。\n",
    "\n",
    "所以这里我们建立输出fold预测方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    " \n",
    "# Some useful parameters which will come in handy later on\n",
    "ntrain = titanic_train_data_X.shape[0]\n",
    "ntest = titanic_test_data_X.shape[0]\n",
    "SEED = 0 #for reproducibility\n",
    "NFOLDS = 7 # set folds for out-of-fold prediction\n",
    "kf = KFold(n_splits = NFOLDS,random_state=SEED,shuffle=True) # 原始为False 但会报错\n",
    " \n",
    "def get_out_fold(clf,x_train,y_train,x_test):\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS,ntest))\n",
    "    \n",
    "    for i, (train_index,test_index) in enumerate(kf.split(x_train)):\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "        \n",
    "        clf.fit(x_tr,y_tr)\n",
    "        \n",
    "        oof_train[test_index] = clf.predict(x_te)\n",
    "        oof_test_skf[i,:] = clf.predict(x_test)\n",
    "        \n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1,1),oof_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建不同的基学习器，这里我们使用了RandomForest、AdaBoost、ExtraTrees、GBDT、DecisionTree、KNN、SVM七个基学习器：（这里的模型可以使用如上面的GridSearch方法对模型的超参数进行搜索选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    " \n",
    "rf = RandomForestClassifier(n_estimators=500,warm_start=True,max_features='sqrt',max_depth=6,min_samples_split=3,min_samples_leaf=2,n_jobs=-1,verbose=0)\n",
    " \n",
    "ada = AdaBoostClassifier(n_estimators=500,learning_rate=0.1)\n",
    " \n",
    "et = ExtraTreesClassifier(n_estimators=500,n_jobs=-1,max_depth=8,min_samples_leaf=2,verbose=0)\n",
    " \n",
    "gb = GradientBoostingClassifier(n_estimators=500,learning_rate=0.008,min_samples_split=3,min_samples_leaf=2,max_depth=5,verbose=0)\n",
    " \n",
    "dt = DecisionTreeClassifier(max_depth=8)\n",
    " \n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    " \n",
    "svm = SVC(kernel='linear',C=0.025)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将pandas转换为arrays：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Numpy arrays of train,test and target(Survived) dataframes to feed into our models\n",
    "x_train = titanic_train_data_X.values   #Creates an array of the train data\n",
    "x_test = titanic_test_data_X.values   #Creates an array of the test data\n",
    "y_train = titanic_train_data_Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete\n"
     ]
    }
   ],
   "source": [
    "# Create our OOF train and test predictions.These base result will be used as new featurs\n",
    "rf_oof_train,rf_oof_test = get_out_fold(rf,x_train,y_train,x_test)  # Random Forest\n",
    "ada_oof_train,ada_oof_test = get_out_fold(ada,x_train,y_train,x_test)  # AdaBoost\n",
    "et_oof_train,et_oof_test = get_out_fold(et,x_train,y_train,x_test)  # Extra Trees\n",
    "gb_oof_train,gb_oof_test = get_out_fold(gb,x_train,y_train,x_test)  # Gradient Boost\n",
    "dt_oof_train,dt_oof_test = get_out_fold(dt,x_train,y_train,x_test)  #Decision Tree\n",
    "knn_oof_train,knn_oof_test = get_out_fold(knn,x_train,y_train,x_test)  # KNeighbors\n",
    "svm_oof_train,svm_oof_test = get_out_fold(svm,x_train,y_train,x_test)  # Support Vector\n",
    " \n",
    "print(\"Training is complete\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  6.4 预测并生成提交文件\n",
    "\n",
    "Level 2：我们利用XGBoost，使用第一层预测的结果作为特征对最终的结果进行预测。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.concatenate((rf_oof_train,ada_oof_train,et_oof_train,gb_oof_train,dt_oof_train,knn_oof_train,svm_oof_train),axis=1)\n",
    "x_test =np.concatenate((rf_oof_test,ada_oof_test,et_oof_test,gb_oof_test,dt_oof_test,knn_oof_test,svm_oof_test),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:27:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    " \n",
    "gbm = XGBClassifier(n_estimators=200,max_depth=4,min_child_weight=2,gamma=0.9,subsample=0.8,\n",
    "                    colsample_bytree=0.8,objective='binary:logistic',nthread=-1,scale_pos_weight=1).fit(x_train,y_train)\n",
    "predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "StackingSubmission = pd.DataFrame({'PassengerId':PassengerId,'Survived':predictions})\n",
    "StackingSubmission.to_csv('StackingSubmission.csv',index=False,sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 验证：学习曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在我们对数据不断地进行特征工程，产生的特征越来越多，用大量的特征对模型进行训练，会使我们的训练集拟合得越来越好，但同时也可能会逐渐丧失泛化能力，从而在测试数据上表现不佳，发生过拟合现象。 当然我们建立的模型可能不仅在预测集上表现不好，也很可能是因为在训练集上的表现不佳，处于欠拟合状态。 下图是在吴恩达老师的机器学习课程上给出的四种学习曲线："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://aliyuntianchipublic.cn-hangzhou.oss-pub.aliyun-inc.com/public/files/image/null/1533521965051_uy6HkOCKQo.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面红线代表test error（Cross-validation error），蓝线代表train error。这里我们也可以把错误率替换为准确率，那么相应曲线的走向就应该是上下颠倒的，（score=1-error）。\n",
    "\n",
    "注意我们的图中是error曲线。\n",
    "\n",
    "1. 左上角是最优情况，随着样本数的增加，train error虽然有一定的增加，但是test error却有很明显的降低；\n",
    "\n",
    "2. 右上角是最差情况，train error很大，模型并没有从特征 中学习到什么，导致test error非常大，模型几乎无法预测数据，需要去寻找数据本身和训练阶段的原因；\n",
    "\n",
    "3. 左下角是high variance，train error虽然较低，但是模型产生了过拟合，缺乏泛化能力，导致test error很高；\n",
    "\n",
    "4. 右下角是high bias的情况，train error很高，这时需要去调整模型的参数，减小train error。\n",
    "\n",
    "所以我们通过学习曲线观察模型处于什么样的状态。从而决定对模型进行如何的操作。当然，我们把验证放到最后，并不是这一步在最后去做。对于我们的Stacking框架中第一层的各个基学习器我们都应该对其学习曲线进行观察，从而去更好地调节超参数，进而得到更好的最终结果。构建绘制学习曲线的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.learning_curve import learning_curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator,title,X,y,ylim=None,cv=None,\n",
    "                        n_jobs=1,train_sizes=np.linspace(.1,1.0,5),verbose=0):\n",
    "    \"\"\"\n",
    "    Generate a simple plot of the test and training learning curve.\n",
    "    \n",
    "    Parameters\n",
    "    -------------\n",
    "    estimator:object type that implents the \"fit\" and \"predict\" methods\n",
    "    An object of that type which is cloned for each validation.\n",
    "    \n",
    "    title:string\n",
    "    Title for the chart.\n",
    "    \n",
    "    X:array-like,shape(n_samples,n_features)\n",
    "    Training vector,where n_samples is the number of samples and n_features is \n",
    "    the number of features.\n",
    "    \n",
    "    y:array-like,shape(n_samples) or (n_samples,n_features),optional\n",
    "    Target relative to X for classification or regression;\n",
    "    None for unsupervised learning.\n",
    "    \n",
    "    ylim:tuple,shape(ymin,ymax),optional\n",
    "    Defines minimum and maximum yvalues plotted.\n",
    "    \n",
    "    cv:integer,cross-validation generator,optional\n",
    "    If an integer is passed,it is the number of folds(defaults to 3).\n",
    "    Specific cross-validation objects can be passed,see\n",
    "    sklearn.cross_validation module for the list of possible objects\n",
    "    \n",
    "    n_jobs:integer,optional\n",
    "    Number of jobs to run in parallel(default 1).\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes,train_scores,test_scores = learning_curve(estimator,X,y,cv=cv,\n",
    "                                                          n_jobs=n_jobs,train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores,axis=1)\n",
    "    train_scores_std = np.std(train_scores,axis=1)\n",
    "    test_scores_mean = np.mean(test_scores,axis=1)\n",
    "    test_scores_std = np.std(test_scores,axis=1)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.fill_between(train_sizes,train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std,alpha=0.1,color='r')\n",
    "    plt.fill_between(train_sizes,test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std,alpha=0.1,color='g')\n",
    "    plt.plot(train_sizes,train_scores_mean,'o-',color=\"r\",label=\"Training score\")\n",
    "    plt.plot(train_sizes,test_scores_mean,'o-',color=\"g\",label=\"Cross-validation score\")\n",
    "    \n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = x_train\n",
    "Y = y_train\n",
    " \n",
    "# RandomForest\n",
    "rf_parameters = {'n_jobs':-1,'n_estimators':500,'warm_start':True,'max_depth':6,\n",
    "                 'min_samples_leaf':2,'max_features':'sqrt','verbose':0}\n",
    " \n",
    "# AdaBoost\n",
    "ada_parameters = {'n_estimators':500,'learning_rate':0.1}\n",
    " \n",
    "# ExtraTrees\n",
    "et_parameters = {'n_jobs':-1,'n_estimators':500,'max_depth':8,'min_samples_leaf':2,'verbose':0}\n",
    " \n",
    "# GradientBoosting\n",
    "gb_parameters = {'n_estimators':500,'max_depth':5,'min_samples_leaf':2,'verbose':0}\n",
    " \n",
    "# DecisionTree\n",
    "dt_parameters = {'max_depth':8}\n",
    " \n",
    "# KNeighbors\n",
    "knn_parameters = {'n_neighbors':2}\n",
    " \n",
    "# SVM\n",
    "svm_parameters = {'kernel':'linear','C':0.025}\n",
    " \n",
    "# XGB\n",
    "gbm_parameters = {'n_estimators':2000,'max_depth':4,'min_child_weight':2,'gamma':0.9,'subsample':0.8,\n",
    "                  'colsample_bytree':0.8,'objective':'binary:logistic','nthread':-1,'scale_pos_weight':1}\n",
    " \n",
    " \n",
    "title = \"Learning Curves\"\n",
    "plot_learning_curve(RandomForestClassifier(**rf_parameters),title,X,Y,cv=None,n_jobs=4,\n",
    "                    train_sizes=[50,100,150,200,250,350,400,450,500])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上面的分析我们可以看出，对于RandomForest的模型，这里是存在一定的问题的，所以我们需要去调整模型的超参数，从而达到更好的效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 超参数调试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将生成的提交文件到Kaggle，得分结果：0.79425\n",
    "\n",
    "xgboost stacking：0.78468\n",
    "\n",
    "voting bagging：0.79904\n",
    "\n",
    "这也说明了我们的stacking模型还有很大的改进空间。所以我们可以在以下几个方面进行改进，提高模型预测的精度：\n",
    "\n",
    "特征工程：寻找更好的特征、删去影响较大的冗余特征；\n",
    "\n",
    "模型超参数调试：改进欠拟合或者过拟合的状态；\n",
    "\n",
    "改进模型框架：对于stacking框架的各层模型进行更好的选择；\n",
    "\n",
    "调参的过程慢慢尝试吧。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (Deep-Learning-with-TensorFlow-book-master)",
   "language": "python",
   "name": "pycharm-bd6f1d8a"
  },
  "language_info": {
   "codemirror_mode": {
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